TUTORIAL
Md Yazid Mohd Saman (Universiti Malaysia Terengganu)
Md Yazid Mohd Saman started his teaching career over 25 years ago in Universiti Pertanian Malaysia (UPM), Serdang, Selangor. He then moved to Kolej Universiti Sains & Teknologi (KUSTEM) in 2001. KUSTEM has been renamed in 2007 as UMT. Currently he is a Professor of Computer Science in the field of Parallel and Distributed Systems. Professor Yazid had published in many international and local journal as well as conference papers. He is contributing his knowledge in supervising numerous young scholar who are pursuing their PhD and Master Degree.
Professor Yazid’s honours include: Member in Bioinformatics Roadmap MIMOS 2005, Member in Grid Computing Roadmap MIMOS 2005, Member in Malaysian Ministry of Education and Malaysian University Colleges, visiting Indian universities and IT companies (IIT Madras, Anna Univ., Chennai, TN, Bangalore, and IIT Delhi), Member in “IT Quality Committee”, Malaysian Ministry of Education, Department of Higher Education, Malaysia, Member of Intl. Soc. for Computers & Their Applications (ISCA), Member in Malaysian Educational Technology Association (Malaysia Educational Technology Association), Journal Paper Assessor for Malaysian Journal Computer Journal, Intl. Journal, Pertanika Malaysia, UUM Journal and Intl. Journal Computer Mathematics UK., Academic Consultant and Advisor, Kolej Teknologi Bestari, Terengganu, Academic Consultant and Advisor, Kolej Universiti Tun Hussin Onn, Session Chair, Intl. Conference ASM’2003, Marbella, Member of Technical Review Panel, Lembaga Akreditasi Negara, Academic Consultant and Advisor, Kolej PTPL , Academic Consultant and Advisor, Cybernetics Technical College, Member of Technical Committee, Intl. Conf. On Information Technology, Australia.
Tutorial Abstract:
Parallel and Grid Computing
Parallel Computing allows more than one processing elements (or processor) to work together as one very fast and efficient system. The main driving force for parallel processing is the desire and prospect for faster solution and greater performance. It is mainly used in solving high computational problems such as weather forecasting, simulation, computational science and other high-compute problems.
Highly parallel computers with a collection of processing elements (PEs) allow each PE to communicate and cooperate to solve large problems fast. We may easily develop cheap parallel computers using off-the-shelves components. However, developing parallel applications for these systems requires thorough programming using specific parallel libraries such as Message Passing Interface (MPI).
Grid Computing or Computational Grids enable the sharing, selection, and aggregation of a wide variety of geographically distributed computational resources (such as supercomputers, compute clusters, storage systems, data sources, instruments, people) and presents them as a single, unified resource for solving large-scale compute and data intensive computing applications.
In this tutorial, participants will be introduced the following issues:
- What is Parallel Computing?
- Developing parallel programs with MPI.
- What is Grid Computing?
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